Building management system with augmented deep learning using combined regression and artificial neural network modeling

ABSTRACT

A building management system is provided. The building management system includes a database, a trust region identifier configured to perform a cluster analysis technique to identify trust regions, and a regression model predictor configured to utilize a regression model technique to calculate a regression model prediction. The building management system further includes a distance metric calculator configured to calculate a distance metric, an artificial neural network model predictor configured to utilize an artificial neural network model technique to calculate an artificial neural network model prediction, and a combined prediction calculator configured to determine a combined prediction based on the distance metric, the regression model prediction, and the artificial neural network model prediction.

CROSS-REFERENCE TO RELATED PATENT APPLICATIONS

This application claims the benefit of and priority to U.S. ProvisionalPatent Application No. 62/540,749, filed Aug. 3, 2017, which isincorporated herein by reference in its entirety.

BACKGROUND

The present disclosure relates generally to the field of heating,ventilation and air conditioning (HVAC) control systems. The presentdisclosure relates more particularly to systems and methods forperforming augmented deep learning (ADL) predictions using combinedregression and artificial neural network (ANN) modeling techniques.

Building management systems may utilize models to predict therelationships between physical plant inputs and outputs. Theserelationships may be utilized in physical plant optimization, controloptimization, fault detection and diagnosis, and various other buildingmanagement analytics. Regression and ANN modeling techniques havecomplementary advantages and disadvantages when utilized to predictthese relationships. For example, regression model predictions can bemade immediately upon startup of the physical plant before a large setof operational data is collected, but tend to be less accurate than ANNmodel predictions. ANN model predictions require access to a larger dataset, but result in more accurate predictions once the data set has beencollected. A building management system that leverages the advantages ofboth modeling techniques would therefore be useful.

SUMMARY

One implementation of the present disclosure is a building managementsystem. The building management system includes a database, a trustregion identifier configured to perform a cluster analysis technique toidentify trust regions, and a regression model predictor configured toutilize a regression model technique to calculate a regression modelprediction. The building management system further includes a distancemetric calculator configured to calculate a distance metric, anartificial neural network model predictor configured to utilize anartificial neural network model technique to calculate an artificialneural network model prediction, and a combined prediction calculatorconfigured to determine a combined prediction based on the distancemetric, the regression model prediction, and the artificial neuralnetwork model prediction.

In some embodiments, the combined prediction calculator uses a weightedaverage or a Kalman filter to determine the combined prediction.

In some embodiments, the distance metric calculator is configured tocalculate the distance metric using plant input data and a clusterdistribution mean or a cluster centroid. In other embodiments, thecluster distribution mean is identified using a Gaussian mixture modeltechnique. In other embodiments, the cluster centroid is identifiedusing a k-means technique.

In some embodiments, the plant input data includes manufacturing dataand offsite data.

Another implementation of the present disclosure is a method foroperating a building management system for a physical plant. The methodincludes creating a regression model using pre-operation data during apre-operational stage of physical plant, identifying multiple dataclusters generated by physical plant data during an operational stage,and determining whether the multiple data clusters exceeds a first datasufficiency threshold. If the multiple data clusters exceeds the firstdata sufficiency threshold, the method includes creating a firstartificial neural network model using the multiple data clusters anddetermining whether new physical plant data meets a first similaritycriterion of at least one of the data clusters. If the new plant datameets a first similarity criterion, the method includes making a firstartificial neural network prediction using the first artificial neuralnetwork model and modifying a characteristic of the physical plantaccording to the first artificial neural network prediction.

In some embodiments, the pre-operation data includes manufacturing dataand offsite data. In some embodiments, the physical plant data includesplant input data or plant output data.

In some embodiments, the first data sufficiency threshold is based on aquantity of physical plant data.

In some embodiments, the method further includes making a regressionmodel prediction using the regression model in response to adetermination that the multiple data clusters does not exceed the firstdata sufficiency threshold and utilizing the regression model predictionto perform a fault detection task, a fault diagnosis task, or a controltask.

In some embodiments, the method further includes utilizing the firstartificial neural network prediction as an input to the regressionmodel. The first artificial neural network prediction is configured toimprove a quality of the regression model.

In some embodiments, the method further includes determining whether themultiple data clusters exceeds a second data sufficiency threshold andcreating a second artificial neural network model using the multipledata clusters in response to a determination that the multiple dataclusters exceeds the second data sufficiency threshold. The methodfurther includes determining whether new physical plant data meets asecond similarity criterion of at least one of the multiple dataclusters, and in response to a determination that the new plant datameets the second similarity criterion, making a second artificial neuralnetwork prediction using the second artificial neural network model. Inother embodiments, the method further includes determining a combinedprediction based on the first artificial neural network prediction andthe second artificial neural network prediction.

Yet another implementation of the present disclosure is a method ofmaking an augmented deep learning model prediction. The method includesreceiving plant input data and plant output data from a physical plant,performing a cluster analysis technique to identify trust regions,calculating a regression model prediction using a regression modeltechnique based on plant input data and plant output data, andcalculating a distance metric. The method further includes calculatingan artificial neural network prediction using an artificial neuralnetwork technique based on plant input data, plant output data, and thedistance metric, determining a combined prediction based on the distancemetric and at least one of the regression model prediction or theartificial neural network prediction, modifying a characteristic of thephysical plant according to the combined prediction.

In some embodiments, determining the combined prediction includes use ofa weighted average or a Kalman filter.

In some embodiments, calculating the distance metric includes use ofplant input data and a cluster distribution mean or a cluster centroid.In other embodiments, the cluster distribution mean is identified usinga Gaussian mixture model technique. In other embodiments, the clustercentroid is identified using a k-means technique.

In some embodiments, the plant input data includes manufacturing data oroffsite data.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a perspective view of a building served by a buildingmanagement system (BMS), according to some embodiments.

FIG. 2 is a block diagram of a waterside system which may be used inconjunction with the BMS of FIG. 1, according to some embodiments.

FIG. 3 is a block diagram of an airside system which may be used inconjunction with the BMS of FIG. 1, according to some embodiments.

FIG. 4 is a block diagram of the BMS of FIG. 1, according to someembodiments.

FIG. 5 is a block diagram of a BMS configured to perform ADL predictionsusing combined regression and ANN modeling techniques, according to someembodiments.

FIG. 6 is a flow diagram illustrating a process for performing ADLpredictions using combined regression and ANN modeling techniques,according to some embodiments.

FIG. 7 is a plot of data clusters used to identify trust regions,according to some embodiments.

FIG. 8 is a plot of ADL predictions for trust regions identified usingGaussian Mixture Modeling (GMM) techniques, according to someembodiments.

FIG. 9 is a plot of ADL predictions for trust regions identified usingcluster centroid (k-means) techniques, according to some embodiments.

DETAILED DESCRIPTION Overview

Before turning to the FIGURES, which illustrate the exemplaryembodiments in detail, it should be understood that the disclosure isnot limited to the details or methodology set forth in the descriptionor illustrated in the figures. It should also be understood that theterminology is for the purpose of description only and should not beregarded as limiting.

Referring generally to the FIGURES, various systems and methods formaking augmented deep learning (ADL) predictions using combinedregression and artificial neural network (ANN) modeling techniques inthe operation of a building management system are shown. The combinationof the modeling techniques leverages the advantages of both: predictionsresulting from regression models are utilized in early operationalstages when a lack of sufficient data makes ANN predictions impossibleor inadvisable, while more accurate ANN predictions are utilized once asufficient body of operational data has been collected. In some cases,regression model predictions are provided as input to the ANN model andvice versa, increasing the quality of the predictions from both theregression and ANN models.

Building Management System and HVAC System

Referring now to FIGS. 1-4, an exemplary building management system(BMS) and HVAC system in which the systems and methods of the presentinvention may be implemented are shown, according to an exemplaryembodiment. Referring particularly to FIG. 1, a perspective view of abuilding 10 is shown, according to an exemplary embodiment. Building 10is serviced by a building management system including a HVAC system 100.HVAC system 100 may include a plurality of HVAC devices (e.g., heaters,chillers, air handling units, pumps, fans, thermal energy storage, etc.)configured to provide heating, cooling, ventilation, or other servicesfor building 10. For example, HVAC system 100 is shown to include awaterside system 120 and an airside system 130. Waterside system 120 mayprovide a heated or chilled fluid to an air handling unit of airsidesystem 130. Airside system 130 may use the heated or chilled fluid toheat or cool an airflow provided to building 10. An exemplary watersidesystem and airside system which may be used in HVAC system 100 aredescribed in greater detail with reference to FIGS. 2-3.

HVAC system 100 is shown to include a chiller 102, a boiler 104, and arooftop air handling unit (AHU) 106. Waterside system 120 may use boiler104 and chiller 102 to heat or cool a working fluid (e.g., water,glycol, etc.) and may circulate the working fluid to AHU 106. In variousembodiments, the HVAC devices of waterside system 120 may be located inor around building 10 (as shown in FIG. 1) or at an offsite locationsuch as a central plant (e.g., a chiller plant, a steam plant, a heatplant, etc.). The working fluid may be heated in boiler 104 or cooled inchiller 102, depending on whether heating or cooling is required inbuilding 10. Boiler 104 may add heat to the circulated fluid, forexample, by burning a combustible material (e.g., natural gas) or usingan electric heating element. Chiller 102 may place the circulated fluidin a heat exchange relationship with another fluid (e.g., a refrigerant)in a heat exchanger (e.g., an evaporator) to absorb heat from thecirculated fluid. The working fluid from chiller 102 and/or boiler 104may be transported to AHU 106 via piping 108.

AHU 106 may place the working fluid in a heat exchange relationship withan airflow passing through AHU 106 (e.g., via one or more stages ofcooling coils and/or heating coils). The airflow may be, for example,outside air, return air from within building 10, or a combination ofboth. AHU 106 may transfer heat between the airflow and the workingfluid to provide heating or cooling for the airflow. For example, AHU106 may include one or more fans or blowers configured to pass theairflow over or through a heat exchanger containing the working fluid.The working fluid may then return to chiller 102 or boiler 104 viapiping 110.

Airside system 130 may deliver the airflow supplied by AHU 106 (i.e.,the supply airflow) to building 10 via air supply ducts 112 and mayprovide return air from building 10 to AHU 106 via air return ducts 114.In some embodiments, airside system 130 includes multiple variable airvolume (VAV) units 116. For example, airside system 130 is shown toinclude a separate VAV unit 116 on each floor or zone of building 10.VAV units 116 may include dampers or other flow control elements thatcan be operated to control an amount of the supply airflow provided toindividual zones of building 10. In other embodiments, airside system130 delivers the supply airflow into one or more zones of building 10(e.g., via supply ducts 112) without using intermediate VAV units 116 orother flow control elements. AHU 106 may include various sensors (e.g.,temperature sensors, pressure sensors, etc.) configured to measureattributes of the supply airflow. AHU 106 may receive input from sensorslocated within AHU 106 and/or within the building zone and may adjustthe flow rate, temperature, or other attributes of the supply airflowthrough AHU 106 to achieve setpoint conditions for the building zone.

Referring now to FIG. 2, a block diagram of a waterside system 200 isshown, according to an exemplary embodiment. In various embodiments,waterside system 200 may supplement or replace waterside system 120 inHVAC system 100 or may be implemented separate from HVAC system 100.When implemented in HVAC system 100, waterside system 200 may include asubset of the HVAC devices in HVAC system 100 (e.g., boiler 104, chiller102, pumps, valves, etc.) and may operate to supply a heated or chilledfluid to AHU 106. The HVAC devices of waterside system 200 may belocated within building 10 (e.g., as components of waterside system 120)or at an offsite location such as a central plant.

In FIG. 2, waterside system 200 is shown as a central plant having aplurality of subplants 202-212. Subplants 202-212 are shown to include aheater subplant 202, a heat recovery chiller subplant 204, a chillersubplant 206, a cooling tower subplant 208, a hot thermal energy storage(TES) subplant 210, and a cold thermal energy storage (TES) subplant212. Subplants 202-212 consume resources (e.g., water, natural gas,electricity, etc.) from utilities to serve the thermal energy loads(e.g., hot water, cold water, heating, cooling, etc.) of a building orcampus. For example, heater subplant 202 may be configured to heat waterin a hot water loop 214 that circulates the hot water between heatersubplant 202 and building 10. Chiller subplant 206 may be configured tochill water in a cold water loop 216 that circulates the cold waterbetween chiller subplant 206 building 10. Heat recovery chiller subplant204 may be configured to transfer heat from cold water loop 216 to hotwater loop 214 to provide additional heating for the hot water andadditional cooling for the cold water. Condenser water loop 218 mayabsorb heat from the cold water in chiller subplant 206 and reject theabsorbed heat in cooling tower subplant 208 or transfer the absorbedheat to hot water loop 214. Hot TES subplant 210 and cold TES subplant212 may store hot and cold thermal energy, respectively, for subsequentuse.

Hot water loop 214 and cold water loop 216 may deliver the heated and/orchilled water to air handlers located on the rooftop of building 10(e.g., AHU 106) or to individual floors or zones of building 10 (e.g.,VAV units 116). The air handlers push air past heat exchangers (e.g.,heating coils or cooling coils) through which the water flows to provideheating or cooling for the air. The heated or cooled air may bedelivered to individual zones of building 10 to serve the thermal energyloads of building 10. The water then returns to subplants 202-212 toreceive further heating or cooling.

Although subplants 202-212 are shown and described as heating andcooling water for circulation to a building, it is understood that anyother type of working fluid (e.g., glycol, CO2, etc.) may be used inplace of or in addition to water to serve the thermal energy loads. Inother embodiments, subplants 202-212 may provide heating and/or coolingdirectly to the building or campus without requiring an intermediateheat transfer fluid. These and other variations to waterside system 200are within the teachings of the present invention.

Each of subplants 202-212 may include a variety of equipment configuredto facilitate the functions of the subplant. For example, heatersubplant 202 is shown to include a plurality of heating elements 220(e.g., boilers, electric heaters, etc.) configured to add heat to thehot water in hot water loop 214. Heater subplant 202 is also shown toinclude several pumps 222 and 224 configured to circulate the hot waterin hot water loop 214 and to control the flow rate of the hot waterthrough individual heating elements 220. Chiller subplant 206 is shownto include a plurality of chillers 232 configured to remove heat fromthe cold water in cold water loop 216. Chiller subplant 206 is alsoshown to include several pumps 234 and 236 configured to circulate thecold water in cold water loop 216 and to control the flow rate of thecold water through individual chillers 232.

Heat recovery chiller subplant 204 is shown to include a plurality ofheat recovery heat exchangers 226 (e.g., refrigeration circuits)configured to transfer heat from cold water loop 216 to hot water loop214. Heat recovery chiller subplant 204 is also shown to include severalpumps 228 and 230 configured to circulate the hot water and/or coldwater through heat recovery heat exchangers 226 and to control the flowrate of the water through individual heat recovery heat exchangers 226.Cooling tower subplant 208 is shown to include a plurality of coolingtowers 238 configured to remove heat from the condenser water incondenser water loop 218. Cooling tower subplant 208 is also shown toinclude several pumps 240 configured to circulate the condenser water incondenser water loop 218 and to control the flow rate of the condenserwater through individual cooling towers 238.

Hot TES subplant 210 is shown to include a hot TES tank 242 configuredto store the hot water for later use. Hot TES subplant 210 may alsoinclude one or more pumps or valves configured to control the flow rateof the hot water into or out of hot TES tank 242. Cold TES subplant 212is shown to include cold TES tanks 244 configured to store the coldwater for later use. Cold TES subplant 212 may also include one or morepumps or valves configured to control the flow rate of the cold waterinto or out of cold TES tanks 244.

In some embodiments, one or more of the pumps in waterside system 200(e.g., pumps 222, 224, 228, 230, 234, 236, and/or 240) or pipelines inwaterside system 200 include an isolation valve associated therewith.Isolation valves may be integrated with the pumps or positioned upstreamor downstream of the pumps to control the fluid flows in watersidesystem 200. In various embodiments, waterside system 200 may includemore, fewer, or different types of devices and/or subplants based on theparticular configuration of waterside system 200 and the types of loadsserved by waterside system 200.

Referring now to FIG. 3, a block diagram of an airside system 300 isshown, according to an exemplary embodiment. In various embodiments,airside system 300 may supplement or replace airside system 130 in HVACsystem 100 or may be implemented separate from HVAC system 100. Whenimplemented in HVAC system 100, airside system 300 may include a subsetof the HVAC devices in HVAC system 100 (e.g., AHU 106, VAV units 116,ducts 112-114, fans, dampers, etc.) and may be located in or aroundbuilding 10. Airside system 300 may operate to heat or cool an airflowprovided to building 10 using a heated or chilled fluid provided bywaterside system 200.

In FIG. 3, airside system 300 is shown to include an economizer-type airhandling unit (AHU) 302. Economizer-type AHUs vary the amount of outsideair and return air used by the air handling unit for heating or cooling.For example, AHU 302 may receive return air 304 from building zone 306via return air duct 308 and may deliver supply air 310 to building zone306 via supply air duct 312. In some embodiments, AHU 302 is a rooftopunit located on the roof of building 10 (e.g., AHU 106 as shown inFIG. 1) or otherwise positioned to receive both return air 304 andoutside air 314. AHU 302 may be configured to operate exhaust air damper316, mixing damper 318, and outside air damper 320 to control an amountof outside air 314 and return air 304 that combine to form supply air310. Any return air 304 that does not pass through mixing damper 318 maybe exhausted from AHU 302 through exhaust damper 316 as exhaust air 322.

Each of dampers 316-320 may be operated by an actuator. For example,exhaust air damper 316 may be operated by actuator 324, mixing damper318 may be operated by actuator 326, and outside air damper 320 may beoperated by actuator 328. Actuators 324-328 may communicate with an AHUcontroller 330 via a communications link 332. Actuators 324-328 mayreceive control signals from AHU controller 330 and may provide feedbacksignals to AHU controller 330. Feedback signals may include, forexample, an indication of a current actuator or damper position, anamount of torque or force exerted by the actuator, diagnosticinformation (e.g., results of diagnostic tests performed by actuators324-328), status information, commissioning information, configurationsettings, calibration data, and/or other types of information or datathat may be collected, stored, or used by actuators 324-328. AHUcontroller 330 may be an economizer controller configured to use one ormore control algorithms (e.g., state-based algorithms, extremum seekingcontrol (ESC) algorithms, proportional-integral (PI) control algorithms,proportional-integral-derivative (PID) control algorithms, modelpredictive control (MPC) algorithms, feedback control algorithms, etc.)to control actuators 324-328.

Still referring to FIG. 3, AHU 302 is shown to include a cooling coil334, a heating coil 336, and a fan 338 positioned within supply air duct312. Fan 338 may be configured to force supply air 310 through coolingcoil 334 and/or heating coil 336 and provide supply air 310 to buildingzone 306. AHU controller 330 may communicate with fan 338 viacommunications link 340 to control a flow rate of supply air 310. Insome embodiments, AHU controller 330 controls an amount of heating orcooling applied to supply air 310 by modulating a speed of fan 338.

Cooling coil 334 may receive a chilled fluid from waterside system 200(e.g., from cold water loop 216) via piping 342 and may return thechilled fluid to waterside system 200 via piping 344. Valve 346 may bepositioned along piping 342 or piping 344 to control a flow rate of thechilled fluid through cooling coil 334. In some embodiments, coolingcoil 334 includes multiple stages of cooling coils that can beindependently activated and deactivated (e.g., by AHU controller 330, byBMS controller 366, etc.) to modulate an amount of cooling applied tosupply air 310.

Heating coil 336 may receive a heated fluid from waterside system200(e.g., from hot water loop 214) via piping 348 and may return theheated fluid to waterside system 200 via piping 350. Valve 352 may bepositioned along piping 348 or piping 350 to control a flow rate of theheated fluid through heating coil 336. In some embodiments, heating coil336 includes multiple stages of heating coils that can be independentlyactivated and deactivated (e.g., by AHU controller 330, by BMScontroller 366, etc.) to modulate an amount of heating applied to supplyair 310.

Each of valves 346 and 352 may be controlled by an actuator. Forexample, valve 346 may be controlled by actuator 354 and valve 352 maybe controlled by actuator 356. Actuators 354-356 may communicate withAHU controller 330 via communications links 358-360. Actuators 354-356may receive control signals from AHU controller 330 and may providefeedback signals to controller 330. In some embodiments, AHU controller330 receives a measurement of the supply air temperature from atemperature sensor 362 positioned in supply air duct 312 (e.g.,downstream of cooling coil 334 and/or heating coil 336). AHU controller330 may also receive a measurement of the temperature of building zone306 from a temperature sensor 364 located in building zone 306.

In some embodiments, AHU controller 330 operates valves 346 and 352 viaactuators 354-356 to modulate an amount of heating or cooling providedto supply air 310 (e.g., to achieve a setpoint temperature for supplyair 310 or to maintain the temperature of supply air 310 within asetpoint temperature range). The positions of valves 346 and 352 affectthe amount of heating or cooling provided to supply air 310 by coolingcoil 334 or heating coil 336 and may correlate with the amount of energyconsumed to achieve a desired supply air temperature. AHU 330 maycontrol the temperature of supply air 310 and/or building zone 306 byactivating or deactivating coils 334-336, adjusting a speed of fan 338,or a combination of both.

Still referring to FIG. 3, airside system 300 is shown to include abuilding management system (BMS) controller 366 and a client device 368.BMS controller 366 may include one or more computer systems (e.g.,servers, supervisory controllers, subsystem controllers, etc.) thatserve as system level controllers, application or data servers, headnodes, or master controllers for airside system 300, waterside system200, HVAC system 100, and/or other controllable systems that servebuilding 10. BMS controller 366 may communicate with multiple downstreambuilding systems or subsystems (e.g., HVAC system 100, a securitysystem, a lighting system, waterside system 200, etc.) via acommunications link 370 according to like or disparate protocols (e.g.,LON, BACnet, etc.). In various embodiments, AHU controller 330 and BMScontroller 366 may be separate (as shown in FIG. 3) or integrated. In anintegrated implementation, AHU controller 330 may be a software moduleconfigured for execution by a processor of BMS controller 366.

In some embodiments, AHU controller 330 receives information from BMScontroller 366 (e.g., commands, setpoints, operating boundaries, etc.)and provides information to BMS controller 366 (e.g., temperaturemeasurements, valve or actuator positions, operating statuses,diagnostics, etc.). For example, AHU controller 330 may provide BMScontroller 366 with temperature measurements from temperature sensors362-364, equipment on/off states, equipment operating capacities, and/orany other information that can be used by BMS controller 366 to monitoror control a variable state or condition within building zone 306.

Client device 368 may include one or more human-machine interfaces orclient interfaces (e.g., graphical user interfaces, reportinginterfaces, text-based computer interfaces, client-facing web services,web servers that provide pages to web clients, etc.) for controlling,viewing, or otherwise interacting with HVAC system 100, its subsystems,and/or devices. Client device 368 may be a computer workstation, aclient terminal, a remote or local interface, or any other type of userinterface device. Client device 368 may be a stationary terminal or amobile device. For example, client device 368 may be a desktop computer,a computer server with a user interface, a laptop computer, a tablet, asmartphone, a PDA, or any other type of mobile or non-mobile device.Client device 368 may communicate with BMS controller 366 and/or AHUcontroller 330 via communications link 372.

Referring now to FIG. 4, a block diagram of a building management system(BMS) 400 is shown, according to an exemplary embodiment. BMS 400 may beimplemented in building 10 to automatically monitor and control variousbuilding functions. BMS 400 is shown to include BMS controller 366 and aplurality of building subsystems 428. Building subsystems 428 are shownto include a building electrical subsystem 434, an informationcommunication technology (ICT) subsystem 436, a security subsystem 438,a HVAC subsystem 440, a lighting subsystem 442, a lift/escalatorssubsystem 432, and a fire safety subsystem 430. In various embodiments,building subsystems 428 can include fewer, additional, or alternativesubsystems. For example, building subsystems 428 may also oralternatively include a refrigeration subsystem, an advertising orsignage subsystem, a cooking subsystem, a vending subsystem, a printeror copy service subsystem, or any other type of building subsystem thatuses controllable equipment and/or sensors to monitor or controlbuilding 10. In some embodiments, building subsystems 428 includewaterside system 200 and/or airside system 300, as described withreference to FIGS. 2-3.

Each of building subsystems 428 may include any number of devices,controllers, and connections for completing its individual functions andcontrol activities. HVAC subsystem 440 may include many of the samecomponents as HVAC system 100, as described with reference to FIGS. 1-3.For example, HVAC subsystem 440 may include a chiller, a boiler, anynumber of air handling units, economizers, field controllers,supervisory controllers, actuators, temperature sensors, and otherdevices for controlling the temperature, humidity, airflow, or othervariable conditions within building 10. Lighting subsystem 442 mayinclude any number of light fixtures, ballasts, lighting sensors,dimmers, or other devices configured to controllably adjust the amountof light provided to a building space. Security subsystem 438 mayinclude occupancy sensors, video surveillance cameras, digital videorecorders, video processing servers, intrusion detection devices, accesscontrol devices and servers, or other security-related devices.

Still referring to FIG. 4, BMS controller 366 is shown to include acommunications interface 407 and a BMS interface 409. Interface 407 mayfacilitate communications between BMS controller 366 and externalapplications (e.g., monitoring and reporting applications 422,enterprise control applications 426, remote systems and applications444, applications residing on client devices 448, etc.) for allowinguser control, monitoring, and adjustment to BMS controller 366 and/orsubsystems 428. Interface 407 may also facilitate communications betweenBMS controller 366 and client devices 448. BMS interface 409 mayfacilitate communications between BMS controller 366 and buildingsubsystems 428 (e.g., HVAC, lighting security, lifts, powerdistribution, business, etc.).

Interfaces 407, 409 can be or include wired or wireless communicationsinterfaces (e.g., jacks, antennas, transmitters, receivers,transceivers, wire terminals, etc.) for conducting data communicationswith building subsystems 428 or other external systems or devices. Invarious embodiments, communications via interfaces 407, 409 may bedirect (e.g., local wired or wireless communications) or via acommunications network 446 (e.g., a WAN, the Internet, a cellularnetwork, etc.). For example, interfaces 407, 409 can include an Ethernetcard and port for sending and receiving data via an Ethernet-basedcommunications link or network. In another example, interfaces 407, 409can include a WiFi transceiver for communicating via a wirelesscommunications network. In another example, one or both of interfaces407, 409 may include cellular or mobile phone communicationstransceivers. In one embodiment, communications interface 407 is a powerline communications interface and BMS interface 409 is an Ethernetinterface. In other embodiments, both communications interface 407 andBMS interface 409 are Ethernet interfaces or are the same Ethernetinterface.

Still referring to FIG. 4, BMS controller 366 is shown to include aprocessing circuit 404 including a processor 406 and memory 408.Processing circuit 404 may be communicably connected to BMS interface409 and/or communications interface 407 such that processing circuit 404and the various components thereof can send and receive data viainterfaces 407, 409. Processor 406 can be implemented as a generalpurpose processor, an application specific integrated circuit (ASIC),one or more field programmable gate arrays (FPGAs), a group ofprocessing components, or other suitable electronic processingcomponents.

Memory 408 (e.g., memory, memory unit, storage device, etc.) may includeone or more devices (e.g., RAM, ROM, Flash memory, hard disk storage,etc.) for storing data and/or computer code for completing orfacilitating the various processes, layers and modules described in thepresent application. Memory 408 may be or include volatile memory ornon-volatile memory. Memory 408 may include database components, objectcode components, script components, or any other type of informationstructure for supporting the various activities and informationstructures described in the present application. According to anexemplary embodiment, memory 408 is communicably connected to processor406 via processing circuit 404 and includes computer code for executing(e.g., by processing circuit 404 and/or processor 406) one or moreprocesses described herein.

In some embodiments, BMS controller 366 is implemented within a singlecomputer (e.g., one server, one housing, etc.). In various otherembodiments BMS controller 366 may be distributed across multipleservers or computers (e.g., that can exist in distributed locations).Further, while FIG. 4 shows applications 422 and 426 as existing outsideof BMS controller 366, in some embodiments, applications 422 and 426 maybe hosted within BMS controller 366 (e.g., within memory 408).

Still referring to FIG. 4, memory 408 is shown to include an enterpriseintegration layer 410, an automated measurement and validation (AM&V)layer 412, a demand response (DR) layer 414, a fault detection anddiagnostics (FDD) layer 416, an integrated control layer 418, and abuilding subsystem integration later 420. Layers 410-420 may beconfigured to receive inputs from building subsystems 428 and other datasources, determine optimal control actions for building subsystems 428based on the inputs, generate control signals based on the optimalcontrol actions, and provide the generated control signals to buildingsubsystems 428. The following paragraphs describe some of the generalfunctions performed by each of layers 410-420 in BMS 400.

Enterprise integration layer 410 may be configured to serve clients orlocal applications with information and services to support a variety ofenterprise-level applications. For example, enterprise controlapplications 426 may be configured to provide subsystem-spanning controlto a graphical user interface (GUI) or to any number of enterprise-levelbusiness applications (e.g., accounting systems, user identificationsystems, etc.). Enterprise control applications 426 may also oralternatively be configured to provide configuration GUIs forconfiguring BMS controller 366. In yet other embodiments, enterprisecontrol applications 426 can work with layers 410-420 to optimizebuilding performance (e.g., efficiency, energy use, comfort, or safety)based on inputs received at interface 407 and/or BMS interface 409.

Building subsystem integration layer 420 may be configured to managecommunications between BMS controller 366 and building subsystems 428.For example, building subsystem integration layer 420 may receive sensordata and input signals from building subsystems 428 and provide outputdata and control signals to building subsystems 428. Building subsystemintegration layer 420 may also be configured to manage communicationsbetween building subsystems 428. Building subsystem integration layer420 translate communications (e.g., sensor data, input signals, outputsignals, etc.) across a plurality of multi-vendor/multi-protocolsystems.

Demand response layer 414 may be configured to optimize resource usage(e.g., electricity use, natural gas use, water use, etc.) and/or themonetary cost of such resource usage in response to satisfy the demandof building 10. The optimization may be based on time-of-use prices,curtailment signals, energy availability, or other data received fromutility providers, distributed energy generation systems 424, fromenergy storage 427 (e.g., hot TES 242, cold TES 244, etc.), or fromother sources. Demand response layer 414 may receive inputs from otherlayers of BMS controller 366 (e.g., building subsystem integration layer420, integrated control layer 418, etc.). The inputs received from otherlayers may include environmental or sensor inputs such as temperature,carbon dioxide levels, relative humidity levels, air quality sensoroutputs, occupancy sensor outputs, room schedules, and the like. Theinputs may also include inputs such as electrical use (e.g., expressedin kWh), thermal load measurements, pricing information, projectedpricing, smoothed pricing, curtailment signals from utilities, and thelike.

According to an exemplary embodiment, demand response layer 414 includescontrol logic for responding to the data and signals it receives. Theseresponses can include communicating with the control algorithms inintegrated control layer 418, changing control strategies, changingsetpoints, or activating/deactivating building equipment or subsystemsin a controlled manner. Demand response layer 414 may also includecontrol logic configured to determine when to utilize stored energy. Forexample, demand response layer 414 may determine to begin using energyfrom energy storage 427 just prior to the beginning of a peak use hour.

In some embodiments, demand response layer 414 includes a control moduleconfigured to actively initiate control actions (e.g., automaticallychanging setpoints) which minimize energy costs based on one or moreinputs representative of or based on demand (e.g., price, a curtailmentsignal, a demand level, etc.). In some embodiments, demand responselayer 414 uses equipment models to determine an optimal set of controlactions. The equipment models may include, for example, thermodynamicmodels describing the inputs, outputs, and/or functions performed byvarious sets of building equipment. Equipment models may representcollections of building equipment (e.g., subplants, chiller arrays,etc.) or individual devices (e.g., individual chillers, heaters, pumps,etc.).

Demand response layer 414 may further include or draw upon one or moredemand response policy definitions (e.g., databases, XML files, etc.).The policy definitions may be edited or adjusted by a user (e.g., via agraphical user interface) so that the control actions initiated inresponse to demand inputs may be tailored for the user's application,desired comfort level, particular building equipment, or based on otherconcerns. For example, the demand response policy definitions canspecify which equipment may be turned on or off in response toparticular demand inputs, how long a system or piece of equipment shouldbe turned off, what setpoints can be changed, what the allowable setpoint adjustment range is, how long to hold a high demand setpointbefore returning to a normally scheduled setpoint, how close to approachcapacity limits, which equipment modes to utilize, the energy transferrates (e.g., the maximum rate, an alarm rate, other rate boundaryinformation, etc.) into and out of energy storage devices (e.g., thermalstorage tanks, battery banks, etc.), and when to dispatch on-sitegeneration of energy (e.g., via fuel cells, a motor generator set,etc.).

Integrated control layer 418 may be configured to use the data input oroutput of building subsystem integration layer 420 and/or demandresponse later 414 to make control decisions. Due to the subsystemintegration provided by building subsystem integration layer 420,integrated control layer 418 can integrate control activities of thesubsystems 428 such that the subsystems 428 behave as a singleintegrated supersystem. In an exemplary embodiment, integrated controllayer 418 includes control logic that uses inputs and outputs from aplurality of building subsystems to provide greater comfort and energysavings relative to the comfort and energy savings that separatesubsystems could provide alone. For example, integrated control layer418 may be configured to use an input from a first subsystem to make anenergy-saving control decision for a second subsystem. Results of thesedecisions can be communicated back to building subsystem integrationlayer 420.

Integrated control layer 418 is shown to be logically below demandresponse layer 414. Integrated control layer 418 may be configured toenhance the effectiveness of demand response layer 414 by enablingbuilding subsystems 428 and their respective control loops to becontrolled in coordination with demand response layer 414. Thisconfiguration may advantageously reduce disruptive demand responsebehavior relative to conventional systems. For example, integratedcontrol layer 418 may be configured to assure that a demandresponse-driven upward adjustment to the setpoint for chilled watertemperature (or another component that directly or indirectly affectstemperature) does not result in an increase in fan energy (or otherenergy used to cool a space) that would result in greater total buildingenergy use than was saved at the chiller.

Integrated control layer 418 may be configured to provide feedback todemand response layer 414 so that demand response layer 414 checks thatconstraints (e.g., temperature, lighting levels, etc.) are properlymaintained even while demanded load shedding is in progress. Theconstraints may also include setpoint or sensed boundaries relating tosafety, equipment operating limits and performance, comfort, fire codes,electrical codes, energy codes, and the like. Integrated control layer418 is also logically below fault detection and diagnostics layer 416and automated measurement and validation layer 412. Integrated controllayer 418 may be configured to provide calculated inputs (e.g.,aggregations) to these higher levels based on outputs from more than onebuilding subsystem.

Automated measurement and validation (AM&V) layer 412 may be configuredto verify that control strategies commanded by integrated control layer418 or demand response layer 414 are working properly (e.g., using dataaggregated by AM&V layer 412, integrated control layer 418, buildingsubsystem integration layer 420, FDD layer 416, or otherwise). Thecalculations made by AM&V layer 412 may be based on building systemenergy models and/or equipment models for individual BMS devices orsubsystems. For example, AM&V layer 412 may compare a model-predictedoutput with an actual output from building subsystems 428 to determinean accuracy of the model.

Fault detection and diagnostics (FDD) layer 416 may be configured toprovide on-going fault detection for building subsystems 428, buildingsubsystem devices (i.e., building equipment), and control algorithmsused by demand response layer 414 and integrated control layer 418. FDDlayer 416 may receive data inputs from integrated control layer 418,directly from one or more building subsystems or devices, or fromanother data source. FDD layer 416 may automatically diagnose andrespond to detected faults. The responses to detected or diagnosedfaults may include providing an alert message to a user, a maintenancescheduling system, or a control algorithm configured to attempt torepair the fault or to work-around the fault.

FDD layer 416 may be configured to output a specific identification ofthe faulty component or cause of the fault (e.g., loose damper linkage)using detailed subsystem inputs available at building subsystemintegration layer 420. In other exemplary embodiments, FDD layer 416 isconfigured to provide “fault” events to integrated control layer 418which executes control strategies and policies in response to thereceived fault events. According to an exemplary embodiment, FDD layer416 (or a policy executed by an integrated control engine or businessrules engine) may shut-down systems or direct control activities aroundfaulty devices or systems to reduce energy waste, extend equipment life,or assure proper control response.

FDD layer 416 may be configured to store or access a variety ofdifferent system data stores (or data points for live data). FDD layer416 may use some content of the data stores to identify faults at theequipment level (e.g., specific chiller, specific AHU, specific terminalunit, etc.) and other content to identify faults at component orsubsystem levels. For example, building subsystems 428 may generatetemporal (i.e., time series) data indicating the performance of BMS 400and the various components thereof. The data generated by buildingsubsystems 428 may include measured or calculated values that exhibitstatistical characteristics and provide information about how thecorresponding system or process (e.g., a temperature control process, aflow control process, etc.) is performing in terms of error from itssetpoint. These processes can be examined by FDD layer 416 to exposewhen the system begins to degrade in performance and alert a user torepair the fault before it becomes more severe.

Augmented Deep Learning Techniques

Turning now to FIG. 5, a block diagram of a BMS 500 configured toperform augmented deep learning (ADL) predictions using combinedregression and artificial neural networks (ANN) modeling techniques isshown. BMS 500 is shown to include a physical plant 502. Physical plant502 represents the physical process, processes, or physics that convertplant inputs into outputs. Plant inputs may be representedmathematically as a vector [u_(i)] with length i, while plant outputsmay be represented as a vector [y_(j)] with length j. For example, ifphysical plant 502 is representative of a central plant, the lengths iand j of the input vector [u_(i)] and output vector [y_(j)] may exceed50.

Inputs [u_(i)] and outputs [y_(j)] may originate from a variety ofsources, including detailed physics-based simulations (e.g., based onmanufacturer data or equipment design models), historical data, andtypical values either known from experience or obtained from similarplants. In various embodiments, the model representing physical plant502 may be linear or non-linear, and in some arrangements, the model maybe of high order. If the physical plant 502 operates dynamically, thenthe output of physical plant 502 will include estimated time constantsso that alignment between steady state inputs and outputs can beobtained. The objective of the ADL prediction is to predict therelationship between the inputs [u_(i)] and outputs [y_(j)] of thephysical plant 502.

The process of making an ADL prediction may be performed by BAScontroller 366, described above with reference to FIGS. 3-4. BAScontroller 366 is shown to include, among other components, a database504, modules related to regression modeling techniques, modules relatedto ANN modeling techniques, and a combined prediction calculator 518that utilizes the combined outputs of the regression modeling modulesand the ANN modeling modules. Database 504 is configured to store plantinputs [u_(i)] and outputs [y_(j)]. In some embodiments, database 504 isadditionally configured to store labeled data for identified dataclusters or trust regions (see trust region identifier 506, described infurther detail below). Labeled data for identified trust regions mayinclude labels, statistics, and centroid locations of trust regions. Insome embodiments, historical data related to physical plant 502 isavailable and can be used as plant input data for filling database 504.In some embodiments, this historical or pre-operation data for thephysical plant 502 includes equipment manufacturer data or datacollected from physical plants other than physical plant 502. In otherembodiments, database 504 is initially empty upon initiation of the ADLprediction process.

Modules of BAS controller 366 related to regression modeling may includea regression model parameter identifier 508 and a regression modelpredictor 510. Regression modeling is a statistical process forestimating the relationship among variables. Typically, regressionmodeling involves minimization of the L₂ norm so that estimated modelparameters will minimize the sum of the prediction errors squared. Anadvantage of regression models is that, when designed properly, loworder models may be used effectively for both interpolation andextrapolation. Another advantage is that regression models are effectiveat modeling dominant relationships between inputs and outputs even whenminimal data is available to estimate parameters. However, regressionmodeling techniques generally have lower predictive power when comparedwith a fully trained ANN model.

In various embodiments, the regression model may use a combination of adeterministic model and a stochastic model. In short, a deterministicmodel is one in which every set of variable states is uniquelydetermined by parameters in the model and by sets of previous states ofthese variables. In other words, a deterministic model always performsthe same way for a given set of initial conditions. By contrast, in astochastic model, which may be alternatively referred to as astatistical model, variable states are not described by unique values,but by probability distributions. Because the stochastic model portionof the regression model operates to drive the error in the model tozero, regression modeling yields good predictions even when relativelylittle data is available to the model. (As described in further detailbelow, ANN modeling yields better predictions when more data isavailable to the model.) Further details of a method for using acombination of deterministic and stochastic models in regressionmodeling may be found in U.S. patent application Ser. No. 14/717,593filed May 20, 2015. The entire disclosure of U.S. patent applicationSer. No. 14/717,593 is incorporated by reference herein.

Regression model parameter identifier 508 is configured to estimate theregression model parameters (i.e., [⊖]) and in some cases pre-processthe plant inputs [u_(i)] to remove non-significant inputs (i.e., astepwise regression technique) and/or reduce the number of model inputsby creating linear combinations of the original inputs (i.e., a latentvariable technique) or by performing input transformations (i.e., aprincipal components analysis (PCA) technique). Further details of a PCAtechnique may be found in U.S. patent application Ser. No. 14/744,761filed Jun. 19, 2015. The entire disclosure of U.S. patent applicationSer. No. 14/744,761 is incorporated by reference herein. The purpose ofthe pre-processing performed by regression model parameter identifier508 is to yield either a low-order physics-based model, also known as a“grey box” model, or a low order empirical model that has goodpredictive power for both interpolation and extrapolation.

Regression model predictor 510 is configured to predict a current output(i.e., [y_(j)]_(pred,reg)) calculated from the plant inputs [u_(i)], orthe modified inputs determined by regression model parameter identifier508. Regression model predictor 510 may utilize any suitable regressionmodeling technique to yield [y_(j)]_(pred,reg). For example, if aprediction of chiller power is desired, a reduced order physics-basedmodel (e.g., a Gordon-Ng Universal Chiller Model) may be utilized.Alternatively, since chiller power is a function of both load and lift,a linear bi-quadratic empirical model may be utilized. In someembodiments, the parameters estimated by regression model parameteridentifier 508 and the predictions executed by regression modelpredictor 510 are performed simultaneously and independently from theANN predictions executed by the ANN modules. In other embodiments, thepredictions from the regression model predictor 510 are provided asinput to the ANN modules to improve the ANN predictions. Similarly, ANNpredictions may be provided as input to the regression modules toimprove the regression model predictions.

Modules of BAS controller 366 related to ANN modeling may include trustregion identifier 506, distance metric calculator 512, ANN trainer 514,and ANN model predictor 516. ANNs, also referred to as “deep learning”or connectionist systems, are computing systems inspired by biologicalneural networks. A typical ANN consists of thousands of interconnectedartificial neurons, which are stacked sequentially in rows known aslayers, forming millions of connections. ANNs may be applied to providenon-linear mapping between inputs and labeled data, and they are veryeffective at modeling complex non-linear relationships even if themodeler has no understanding of the process being modeled. However, ANNshave several implementation and operational disadvantages. Onedisadvantage is that a large volume of training data, including labeleddata, is required and significant computational resources are requiredfor training. Significant resources may also be involved if a human isrequired to label the data. Further disadvantages of ANNs include thefact that they do not provide a causal explanation of the relationshipbetween the inputs and the resulting predictions. They are alsounsuitable for extrapolation and interpolation in regions where trainingdata was sparse or absent since predicted outputs can often have littlerelationship with the physical reality.

Trust region identifier 506 may be configured to receive stored plantinputs [u_(i)] from database 504 and employ cluster analysis to identifydata clusters within the i dimensional hyperspace. Options for thecluster analysis technique include, but are not limited to, GaussianMixture Model (GMM) and k-means techniques. In various embodiments,trust region identifier 506 is configured to give each identifiedcluster or trust region a label so that it can be uniquely identified.Regardless of the cluster analysis technique utilized, over time and asadditional data becomes available, new trust regions will be created,older trust regions will consolidate, and voids in the i dimensionalhyperspace will be reduced until eventually the entire hyperspace isspanned by a single trust region. In some embodiments, trust regionidentifier 506 operates independently of the application consuming theADL predictions.

GMMs are composed of multiple multivariate normal density functions. Foreach cluster, the GMM provides both an i dimensional mean vector and ani x i dimensional covariance matrix that are useful for understandingboth the cluster location and how the data is distributed within thecluster. In some embodiments, GMMs utilize posterior probabilities todetermine member in a cluster. The “best” number of clusters within thei dimensional hyperspace can be determined using a variety oftechniques, including Principal Component Analysis (PCA) or AkaikeInformation Criterion (AIC). By contrast, k-means clustering determinesmembership by minimizing distances from points to the mean or medianlocation of its assigned cluster. For each cluster, the k-meanstechnique provides the centroid location. In addition, the total sum ofthe distances may be utilized to determine the ideal number of clustersto be identified.

Still referring to FIG. 5, ANN trainer 512 may be configured to trainthe ANN for each identified trust region. Periodically, new datapreviously identified as belonging to a trust region (i.e., by trustregion identifier 506) may be utilized to provide additional trainingdata for the associated ANNs. This additional training data allows thepredictive power of the ANNs to increase over time. In some embodiments,ANN trainer 514 operates offline and independently with respect to theapplication consuming the ADL predictions.

Distance metric calculator 514 may be configured to calculate a distancemetric between a given input [u_(i)] and a nearby trust region. Forexample, the distance metric may be between an input [u_(i)] and acluster distribution mean identified via a GMM technique or a clustercentroid identified via a k-means technique. The distance metric may becalculated using any suitable technique (e.g., a Euclidean distance, aMahalanobis distance). ANN model predictor 516 is configured tocalculate ANN predictions [y_(j)]_(pred,ANN) of nearby trust regions.Classification of “nearby” may be determined based on the distancemetric determined by the distance metric calculator 514, or, in the caseof trust regions identified by a GMM technique, the distance metric andco-variance information. In some embodiments, both distance metriccalculator 514 and ANN model predictor 516 are configured to operatesynchronously with the application consuming the ADL predictions.

Combined prediction calculator 518 is configured to determine ADLpredictions based on the regression model prediction input (i.e.,[y_(j)]_(pred,reg)) received from regression model predictor 510 and theANN model prediction input (i.e., [y_(j)]_(pred,ANN)) received from ANNmodel predictor 516. Any suitable technique may be utilized to combinethe regression and ANN model predictions. For example, combinationtechniques may include, but are not limited to, Kalman filtering, linearcombinations, and non-linear combinations. The combined predictioncalculator 518 may be configured to operate synchronously with theapplication consuming the ADL predictions. Further details regarding thecombined ADL predictions are included below with reference to FIGS. 8-9.

Referring now to FIG. 6, a flow diagram illustrating a process 600 forusing augmented deep learning techniques to make modeling predictions isshown. In some embodiments, process 600 is performed by BMS controller366 of BMS 400. Process 600 is shown to begin with step 602, in whichdatabase 504 receives physical outputs from the physical plant 502. Insome embodiments, the physical outputs are a vector [y_(j)] with lengthj. At step 604, trust region identifier 506 performs cluster analysismethods to identify trust regions (e.g., via a GMM or a k-factorstechnique).

At step 606, regression model parameter identifier 508 estimates theparameters for the regression model. In some embodiments, step 606includes removing non-significant inputs or reducing the number of modelinputs via linear combinations of the original inputs and/or inputtransformations. Continuing with step 608, regression model predictor510 calculates regression model predictions (i.e., [y_(j)]_(pred,reg))based on parameter input received from regression model parameteridentifier 508. In various embodiments, the regression model predictionincludes both a deterministic and a stochastic component and may becalculated via a variety of regression model techniques (e.g., a reducedorder physics-based model, a linear quadratic empirical model).

At step 610, ANN trainer 512 receives inputs from database 504 and trustregion identifier 506 to train the ANN for each identified trust region.Periodically providing ANN trainer 512 with new data may increase thepredictive power of the ANN model over time. In some embodiments, step610 is not performed until one or more data sufficiency thresholds isexceeded. In various embodiments, the data sufficiency threshold may bebased on the amount of data received from the database 504. The amountof data stored in the database 504 (e.g., plant input data , plantoutput data) may be related to the amount of time the physical plant 502has been operational. Continuing with step 612, the distance metriccalculator 514 calculates a distance metric (e.g., a Euclidean distance,a Mahalanobis distance) between the inputs [u_(i)] and the clusterdistribution means (i.e., if a GMM technique has been utilized toidentify the trust regions) or the cluster centroids (i.e., if a k-meanstechnique has been utilized to identify the trust regions). At step 614,ANN model predictor 516 makes an ANN model prediction based on inputreceived from ANN trainer 512 and distance metric calculator 514. Asdescribed above, in some embodiments, the ANN model prediction may beutilized as an input to the regression model predictor 510 to improvethe quality of the regression model predictions.

In some embodiments, the steps comprising the regression modelprediction (i.e., steps 606 and 608) occur simultaneously with the stepscomprising the ANN model prediction (i.e., steps 610-614). In otherembodiments, the steps comprising the regression model prediction areperformed during a pre-operational stage of the physical plant 502 andbefore sufficient data has been collected from the physical plant 502 toperform the steps comprising the ANN model prediction.

Process 600 concludes at step 616 as combined prediction calculator 518utilizes the regression model prediction (i.e., [y_(j)]_(pred,reg)) andthe ANN model prediction (i.e., [y_(j)]_(pred,ANN)) to determine an ADLprediction. In various embodiments, combined prediction calculator 518uses any suitable technique (e.g., Kalman filters, linear combinations,non-linear combinations) to determine the combined prediction from theregression model prediction and the ANN model prediction. In variousembodiments, the combined prediction calculator 518 may utilize theregression model prediction, one ANN model prediction, multiple ANNpredictions, or any combination thereof to determine the ADL prediction.The ADL prediction may be utilized to modify an operating characteristicof the physical plant 502. For example, the ADL prediction may be usedto optimize control of the equipment in HVAC system 100, watersidesystem 200, or airside system 300. In other embodiments, the ADLprediction can be used to perform fault detection tasks, faultdiagnostic tasks, or other tasks related to analytics.

Referring now to FIG. 7, a plot 700 of trust regions as identified by ak-means technique is depicted, according to some embodiments. Asdescribed above, trust regions may include regions of i dimensionalhyperspace (represented in plot 700 by axes 702, 704, and 706) whereinput [u_(i)] is sufficiently dense to train an ANN. As shown in theplot 700, data is clustered in four discrete regions 708, 710, 712, and714, which may lead to the identification of four trust regions. In someembodiments, the k-means technique is performed by trust regionidentifier 506.

Turning now to FIG. 8, a plot 800 of ADL predictions for trust regionsidentified using Gaussian Mixture Modeling (GMM) techniques is shown,according to some embodiments. As shown, plot 800 depicts a first trustregion 802 and a second trust region 804 plotted along first axis 806and second axis 808. The trust regions 802 and 804 may be utilized assimilarity criteria to determine whether one or more ANN modelpredictions should be utilized in whole or in part in the ADLprediction. For example, if input [u_(i)] is located within the 68%confidence limits of the nearest trust region (i.e., Point A,represented by 810), then the ANN model prediction is used exclusivelywithout regard to the regression model prediction:

[y _(j)]_(pred,ADL)(u _(i))=[y _(j)]_(pred,ANN)(u _(i))

By contrast, if input [u_(i)] is located outside of the 99% confidencelimits of the nearest trust region (i.e., Point B, represented by 812),then the regression model prediction is used exclusively without regardto the ANN model prediction:

[y _(j)]_(pred,ADL)(u _(i))=[y _(j)]_(pred,reg)(u _(i))

If, however, input [u_(i)] is located between the 68% and 99% confidencelimits of the nearest trust region (i.e., Point C, represented by 814),then the ADL model prediction is a continuous function of both the ANNmodel prediction and the regression model prediction: For example, insome embodiments, the continuous function includes a weighted average ora Kalman filter.

[y _(j)]_(pred,ADL)(u _(i))=f([y _(j)]_(pred,ANN)(u _(i)),[y_(j)]_(pred,reg)(u _(i)))

Referring now to FIG. 9, a plot 900 of ADL predictions for trust regionsidentified using cluster centroid (k-means) techniques is shown,according to some embodiments. As shown, plot 900 depicts a first trustregion 902 and a second trust region 904 plotted along first axis 906and second axis 908. The trust regions 902 and 904 may be utilized assimilarity criteria to determine whether one or more ANN modelpredictions should be utilized in whole or in part in the ADLprediction. If input [u_(i)] is located within a predetermined distancefrom the nearest trust region centroid (i.e., inside the region 910bounded by the vertically-oriented ellipse), then the ANN prediction isused exclusively without regard to the regression model prediction:

[y _(j)]_(pred,ADL)(u _(i))=[y _(j)]_(pred,ANN)(u _(i))

Conversely, if input [u_(i)] is located outside the second predetermineddistance from the nearest trust region centroid (i.e., outside theregion 912 bounded by the horizontally-oriented ellipse), then theregression model prediction is used exclusively without regard to theANN model prediction:

[y _(j)]_(pred,ADL)(u _(i))=[y _(j)]_(pred,reg)(u _(i))

If, however, input [u_(i)] is located in the region between the boundedtrust regions (i.e., between regions 910 and 912), then the ADL modelprediction is a continuous function of both the ANN model prediction andthe regression model prediction based on the distance of the input[u_(i)] from the cluster centroid:

[y _(j)]_(pred,ADL)(u _(i))=f([y _(j)]_(pred,ANN)(u _(i)), [y_(j)]_(pred,reg)(u _(i)), distance )

Although the systems and methods described above have been describedexclusively with reference to control of the environmental conditions ofa building via a building management system (e.g., making predictions ofa required chiller power), ADL predictions made from a combination ofregression and ANN model predictions may be utilized in a variety ofapplications. For example, the ADL prediction techniques describedherein may be useful in the fields of video processing, imagerecognition, object identification, threat modeling, fault detection,and industrial configuration optimization.

Configuration of Exemplary Embodiments

The construction and arrangement of the systems and methods as shown inthe various exemplary embodiments are illustrative only. Although only afew embodiments have been described in detail in this disclosure, manymodifications are possible. For example, the position of elements may bereversed or otherwise varied and the nature or number of discreteelements or positions may be altered or varied. Accordingly, all suchmodifications are intended to be included within the scope of thepresent disclosure. The order or sequence of any process or method stepsmay be varied or re-sequenced according to alternative embodiments.Other substitutions, modifications, changes, and omissions may be madein the design, operating conditions and arrangement of the exemplaryembodiments without departing from the scope of the present disclosure.

The present disclosure contemplates methods, systems and programproducts on any machine-readable media for accomplishing variousoperations. The embodiments of the present disclosure may be implementedusing existing computer processors, or by a special purpose computerprocessor for an appropriate system, incorporated for this or anotherpurpose, or by a hardwired system. Embodiments within the scope of thepresent disclosure include program products comprising machine-readablemedia for carrying or having machine-executable instructions or datastructures stored thereon. Such machine-readable media can be anyavailable media that can be accessed by a general purpose or specialpurpose computer or other machine with a processor. By way of example,such machine-readable media can comprise RAM, ROM, EPROM, EEPROM, CD-ROMor other optical disk storage, magnetic disk storage or other magneticstorage devices, or any other medium which can be used to carry or storedesired program code in the form of machine-executable instructions ordata structures and which can be accessed by a general purpose orspecial purpose computer or other machine with a processor. Combinationsof the above are also included within the scope of machine-readablemedia. Machine-executable instructions include, for example,instructions and data which cause a general purpose computer, specialpurpose computer, or special purpose processing machines to perform acertain function or group of functions.

Although the figures show a specific order of method steps, the order ofthe steps may differ from what is depicted. Also two or more steps maybe performed concurrently or with partial concurrence. Such variationwill depend on the software and hardware systems chosen and on designerchoice. All such variations are within the scope of the disclosure.Likewise, software implementations could be accomplished with standardprogramming techniques with rule based logic and other logic toaccomplish the various connection steps, processing steps, comparisonsteps and decision steps.

What is claimed is:
 1. A building management system comprising: adatabase configured to store at least one of plant input data, plantoutput data, or identified trust region data; a trust region identifierconfigured to perform a cluster analysis technique to identify trustregions; a regression model predictor configured to utilize a regressionmodel technique to calculate a regression model prediction based atleast in part on plant input data and plant output data; a distancemetric calculator configured to calculate a distance metric; anartificial neural network model predictor configured to utilize anartificial neural network model technique to calculate an artificialneural network model prediction based at least in part on plant inputdata, plant output data, and the distance metric; and a combinedprediction calculator configured to determine a combined predictionbased on the distance metric and at least one of the regression modelprediction or the artificial neural network model prediction.
 2. Thebuilding management system of claim 1, wherein the combined predictioncalculator uses at least one of a weighted average or a Kalman filter todetermine the combined prediction.
 3. The building management system ofclaim 1, wherein the distance metric calculator is configured tocalculate the distance metric using plant input data and at least one ofa cluster distribution mean or a cluster centroid.
 4. The buildingmanagement system of claim 3, wherein the cluster distribution mean isidentified using a Gaussian mixture model technique.
 5. The buildingmanagement system of claim 3, wherein the cluster centroid is identifiedusing a k-means technique.
 6. The building management system of claim 1,wherein the plant input data comprises at least one of manufacturingdata and offsite data.
 7. A method for operating a building managementsystem for a physical plant, the method comprising: creating aregression model using pre-operation data during a pre-operational stageof physical plant; identifying a plurality of data clusters generated byphysical plant data during an operational stage of the physical plant;determining whether the plurality of data clusters exceeds a first datasufficiency threshold; in response to a determination that the pluralityof data clusters exceeds the first data sufficiency threshold, creatinga first artificial neural network model using the plurality of dataclusters; determining whether new physical plant data meets a firstsimilarity criterion of at least one of the plurality of data clusters;in response to a determination that the new plant data meets the firstsimilarity criterion, making a first artificial neural networkprediction using the first artificial neural network model; andmodifying a characteristic of the physical plant according to the firstartificial neural network prediction.
 8. The method of claim 7, whereinthe pre-operation data comprises at least one of manufacturing data andoffsite data.
 9. The method of claim 7, wherein the physical plant datacomprises at least one of plant input data or plant output data.
 10. Themethod of claim 7, wherein the first data sufficiency threshold is basedon a quantity of physical plant data.
 11. The method of claim 7, whereinthe method further comprises: in response to a determination that theplurality of data clusters does not exceed the first data sufficiencythreshold, making a regression model prediction using the regressionmodel; utilizing the regression model prediction to perform at least oneof a fault detection task, a fault diagnosis task, or a control task.12. The method of claim 7, wherein the method further comprisesutilizing the first artificial neural network prediction as an input tothe regression model, the first artificial neural network predictionconfigured to improve a quality of the regression model.
 13. The methodof claim 7, wherein the method further comprises: determining whetherthe plurality of data clusters exceeds a second data sufficiencythreshold; in response to a determination that the plurality of dataclusters exceeds the second data sufficiency threshold, creating asecond artificial neural network model using the plurality of dataclusters; determining whether new physical plant data meets a secondsimilarity criterion of at least one of the plurality of data clusters;and in response to a determination that the new plant data meets thesecond similarity criterion, making a second artificial neural networkprediction using the second artificial neural network model.
 14. Themethod of claim 13, wherein the method further comprises determining acombined prediction based on the first artificial neural networkprediction and the second artificial neural network prediction.
 15. Amethod of making an augmented deep learning model prediction comprising:receiving plant input data and plant output data from a physical plant;performing a cluster analysis technique to identify trust regions;calculating a regression model prediction using a regression modeltechnique based at least in part on plant input data and plant outputdata; calculating a distance metric; calculating an artificial neuralnetwork prediction using an artificial neural network technique based atleast in part on plant input data, plant output data, and the distancemetric; determining a combined prediction based on the distance metricand at least one of the regression model prediction or the artificialneural network prediction; and modifying a characteristic of thephysical plant according to the combined prediction.
 16. The method ofclaim 15, wherein determining the combined prediction comprises use ofat least one of a weighted average or a Kalman filter.
 17. The method ofclaim 15, wherein calculating the distance metric comprises use of plantinput data and at least one of a cluster distribution mean or a clustercentroid.
 18. The method of claim 17, wherein the cluster distributionmean is identified using a Gaussian mixture model technique.
 19. Themethod of claim 17, wherein the cluster centroid is identified using ak-means technique.
 20. The method of claim 15, wherein the plant inputdata comprises at least one of manufacturing data and offsite data.